Analysis of E-Scooter Vibrations Risks for Riding Comfort Based on Real Measurements

Abstract

Means of transport should be able to fulfil their main function safely and comfortably for travellers and drivers. The effects of vibrations on ride comfort are in the frequency range of 0.5 to 80 Hz and can be analysed using the UNE-2631 standard. This type of analysis has been conducted for several means of transport (bicycles, motorcycles, cars, trucks, etc.), but the literature on e-scooter comfort is very scarce. Existing research describes methodologies, simulation models, and a few measurements related to e-scooter comfort. This paper presents, for the first time, a comfort analysis using an Arduino-based data acquisition system at a sampling frequency of 200 Hz (higher than that in previous studies). Acceleration and speed measurements were obtained by sensorising an e-scooter with inflated wheels without any additional damping systems, which is one of the commonly used e-scooter types. In this study, the comfort for two different speeds (20 and 28 km/h), two types of pavements (pavers and asphalt), and two drivers with different weights was investigated. The results indicate the lowest comfort values for higher velocities and paver pavement. Furthermore, the comfort values were extremely low for all scenarios. In addition, the results demonstrate the necessity of using a sampling rate of at least 80 Hz for this e-scooter model.

Sensitivity of Mass Geometry Parameters on E-Scooter Comfort: Design Guide

“…Thus, it is proposed for existing models to add an acceleration sensor that could be placed below the foot of driver, fixed to the e-scooter frame just below the central zone of the e-scooter treadable zone, in the centre if possible. The sensor could be a low-cost sensor, such as the ADXL335 triaxial accelerometer used in other e-scooter experiments [ 34 ]. E-scooter manufacturers should power this sensor and read it at least at 80 Hz [ 34 ] (200 Hz if possible [ 37 ]) and postprocess the accelerations following the recommendations of the standard UNE-2631 [ 37 ], explained hereinbefore.…”

“…The sensor could be a low-cost sensor, such as the ADXL335 triaxial accelerometer used in other e-scooter experiments [ 34 ]. E-scooter manufacturers should power this sensor and read it at least at 80 Hz [ 34 ] (200 Hz if possible [ 37 ]) and postprocess the accelerations following the recommendations of the standard UNE-2631 [ 37 ], explained hereinbefore. Based on Table 3 , where the comfort scale has been collected, we propose here different colours for advising the e-scooter driver about the received vibration.…”

“…Antoniazzi and Davoli [ 33 ] applied the methodology suggested by Cano-Moreno et al [ 21 ] and measured the vibrations received by e-scooters on different pavements and at different speeds to evaluate their influence on driver comfort. Cano-Moreno et al [ 34 , 35 ] also sensorized a Citycross model scooter, thus obtaining the influence on comfort and health of real measurements of accelerations using an Arduino as data acquisition equipment. The results of all these studies are consistent and coherent with each other and with those obtained in the dynamic simulation model.…”

Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research

“…Other studies have used sensors based on Raspberry Pi or Arduino installed on an e-scooter. Cano-Moreno et al [ 19 ] used accelerometers with a data collection frequency of 200 Hz, which is too high and storage-consuming. Ma et al [ 16 ] used an inertial measurement unit (IMU) combining a three-axis gyroscope and three-axis accelerometer, being consistent with the system proposed in this study.…”

“…E-scooter riding comfort has been also analysed using an Arduino-based data acquisition system at a sampling frequency of 200 Hz, allowing the collection of acceleration and speed measurements [ 19 ]. The system was based on the Nano 3.x microcontroller from Arduino, which converted the signal from the accelerometers and AH49E Hall-effect linear magnetic detector through analogue–digital converters into integer values that were stored in text files on an SD card.…”